" ...
Here we model odor supported place cells by using a simple feed-forward network and analyze the impact of olfactory cues on place cell formation and spatial navigation.
The obtained place cells are used to solve a goal navigation task by a novel mechanism based on self-marking by odor patches combined with a Q-learning algorithm.
We also analyze the impact of place cell remapping on goal directed behavior when switching between two environments.
..."
Reference: 1 .
Kulvicius T, Tamosiunaite M, Ainge J, Dudchenko P, Worgotter F (2008) Odor supported place cell model and goal navigation in rodents. J Comput Neurosci25:481-500 [PubMed]

Model Information (Click on a link to find other models with that property)

This is the readme for the matlab scripts associated with the paper:
Kulvicius T, Tamosiunaite M, Ainge J, Dudchenko P, Worgotter F (2008)
Odor supported place cell model and goal navigation in rodents.
J Comput Neurosci 25:481-500
navPFdevelv03.m - place field model implemented by using feed-forward
network with winner takes all learning
algorithm. Place cells are formed from visual
and olfactory input.
createCellsAll.m - creates place fields. This script is called by
"navPFdevelv03.m".
plotPFM.m - plots place field maps. This function is called by
"navPFdevelv03.m".
navPFQLv14.m - goal navigation based on palce cells and Q-learning
with function aproximation.
navUrineBasedv04.m - goal navigation based on self generated odor
marks (no place fields).
navPFQLUv07.m - goal navigation using combined navigation algorithm
(Q-learning based on place cells + self-marking
navigation)
These files were supplied by Tomas Kulvicius.